Zobrazeno 1 - 10
of 9 581
pro vyhledávání: '"Althoff A"'
Autor:
Gassert, Philipp, Althoff, Matthias
Reinforcement learning (RL) is not yet competitive for many cyber-physical systems, such as robotics, process automation, and power systems, as training on a system with physical components cannot be accelerated, and simulation models do not exist or
Externí odkaz:
http://arxiv.org/abs/2410.23419
Autor:
Narayanswamy, Girish, Liu, Xin, Ayush, Kumar, Yang, Yuzhe, Xu, Xuhai, Liao, Shun, Garrison, Jake, Tailor, Shyam, Sunshine, Jake, Liu, Yun, Althoff, Tim, Narayanan, Shrikanth, Kohli, Pushmeet, Zhan, Jiening, Malhotra, Mark, Patel, Shwetak, Abdel-Ghaffar, Samy, McDuff, Daniel
Wearable sensors have become ubiquitous thanks to a variety of health tracking features. The resulting continuous and longitudinal measurements from everyday life generate large volumes of data; however, making sense of these observations for scienti
Externí odkaz:
http://arxiv.org/abs/2410.13638
Autor:
Tang, Chencheng, Althoff, Matthias
Formal verification of robotic tasks requires a simple yet conformant model of the used robot. We present the first work on generating reachset conformant models for robotic contact tasks considering hybrid (mixed continuous and discrete) dynamics. R
Externí odkaz:
http://arxiv.org/abs/2410.10391
Calculating the inverse kinematics (IK) is fundamental for motion planning in robotics. Compared to numerical or learning-based approaches, analytical IK provides higher efficiency and accuracy. However, existing analytical approaches require manual
Externí odkaz:
http://arxiv.org/abs/2409.14815
Autor:
Gu, Ken, Shang, Ruoxi, Jiang, Ruien, Kuang, Keying, Lin, Richard-John, Lyu, Donghe, Mao, Yue, Pan, Youran, Wu, Teng, Yu, Jiaqian, Zhang, Yikun, Zhang, Tianmai M., Zhu, Lanyi, Merrill, Mike A., Heer, Jeffrey, Althoff, Tim
Data-driven scientific discovery requires the iterative integration of scientific domain knowledge, statistical expertise, and an understanding of data semantics to make nuanced analytical decisions, e.g., about which variables, transformations, and
Externí odkaz:
http://arxiv.org/abs/2408.09667
Reinforcement learning often uses neural networks to solve complex control tasks. However, neural networks are sensitive to input perturbations, which makes their deployment in safety-critical environments challenging. This work lifts recent results
Externí odkaz:
http://arxiv.org/abs/2408.09112
Adjusting robot behavior to human preferences can require intensive human feedback, preventing quick adaptation to new users and changing circumstances. Moreover, current approaches typically treat user preferences as a reward, which requires a manua
Externí odkaz:
http://arxiv.org/abs/2408.06105
Autor:
Lützow, Laura, Althoff, Matthias
Formal verification techniques play a pivotal role in ensuring the safety of complex cyber-physical systems. To transfer model-based verification results to the real world, we require that the measurements of the target system lie in the set of reach
Externí odkaz:
http://arxiv.org/abs/2407.11692
Large language models (LLMs) are being applied to time series forecasting. But are language models actually useful for time series? In a series of ablation studies on three recent and popular LLM-based time series forecasting methods, we find that re
Externí odkaz:
http://arxiv.org/abs/2406.16964
Autor:
Paruchuri, Akshay, Garrison, Jake, Liao, Shun, Hernandez, John, Sunshine, Jacob, Althoff, Tim, Liu, Xin, McDuff, Daniel
Language models (LM) are capable of remarkably complex linguistic tasks; however, numerical reasoning is an area in which they frequently struggle. An important but rarely evaluated form of reasoning is understanding probability distributions. In thi
Externí odkaz:
http://arxiv.org/abs/2406.12830